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Rennes, France Inria Temps pleinLe descriptif de l’offre ci-dessous est en Anglais_ **Type de contrat**: CDD **Niveau de diplôme exigé**: Thèse ou équivalent **Fonction**: Post-Doctorant **A propos du centre ou de la direction fonctionnelle**: The Inria Centre at Rennes University is one of Inria's nine centres and has more than thirty research teams. The Inria Centre is a major and...
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Post-doctoral Research Visit F/m Data Assimilation
il y a 2 semaines
Le descriptif de l’offre ci-dessous est en Anglais_ **Type de contrat **:CDD**Niveau de diplôme exigé **:Thèse ou équivalent**Fonction **:Post-Doctorant**Niveau d'expérience souhaité **:Jeune diplôméA propos du centre ou de la direction fonctionnelle The Inria Rennes - Bretagne Atlantique Centre is one of Inria's eight centres and has more than thirty research teams. The Inria Center is a major and recognized player in the field of digital sciences. It is at the heart of a rich R&D and innovation ecosystem: highly innovative PMEs, large industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute, etc. Contexte et atouts du poste The Odyssey team is offering a 18 month postdoc position on ocean modelling. Odyssey (for Ocean DYnamicS obSErvation analYsis) is a recently created team involving researchers from Inria (Rennes, France), Ifremer (Brest) and IMT Atlantique (Brest). Inria is one of the leading research institute in Computer Sciences in France, and Odyssey is also affiliated to the mathematics research institute of the Rennes University (IRMAR). The team expertise encompasses mathematical (stochastic) and numerical modelling of ocean flows, observational and physical oceanography, data assimilation and machine learning. Gathering this large panel of skills, the team aims at improving our understanding, reconstruction and forecasting of ocean dynamics, and more specifically to bridge model-driven and observation-driven paradigms to develop and learn novel representations of the coupled ocean-atmosphere dynamics ocean models. Mission confiée For accurate climatic predictions, it is essential to have plausible forecasts of the future ocean state.Ideally, high-resolution ocean simulations would be used for this purpose. However, due to their associated computational costs, this approach is currently infeasible, and we must rely only on large-scale ocean representations. To address this challenge and the urgent need to generate various likely scenarios, there has been a growing interest in geophysical sciences and climate studies in developing flow models that incorporate noise to account for modelling uncertainties or errors. The introduction of noise into ocean dynamics models must be done on a theoretically rigorous ground. Ad-hoc choices for model noise can fundamentally disrupt the corresponding fluid dynamics models, leading to unrealistic properties. Rigorously justified methodologies for deriving stochastic dynamics models have been recently introduced in the Odyssey team within the ERC STUOD and a longstanding collaboration with Imperial College and Ifremer. The theoretical framework on which we rely, referred to as "modelling under location uncertainty", decomposes the flow in terms of a resolved smooth component and a rapidly oscillating random component.The stochastic dynamics is then defined from a stochastic representation of the Reynolds transport theorem.From this modelling principle, stochastic equivalents of the classical geophysical flow models can be defined. A set of models ranging from multi-layers quasi-geostrophic models to primitive equations have been in this way defined and numerically implemented. Ensemble data assimilation are currently under development as well as simplified ocean atmosphere coupled models. The present post-doc position aim to explore: data driven dynamics specification and learning from high-resolution data as well as the devising of hierarchical data assimilation ensemble strategies to couple stochastic ocean model and high resolution satellite data such as the SWOT data Principales activités The post-doc will collaborate directly with the Odyssey group in Rennes (E. Mémin, Noé Lahaye and Gilles Tissot).He/She will be part of a small group devoted to ensemble method for forecast, learning and data assimilation of ocean dynamics. Her/His work will undergo also strong collaborations with the Odyssey group at IMT Atlantique (R. Fablet) as well as with the other PI of the ERC Stuod group (Bertrand Chapron, Dan Crisan, Darryl Holm). In this post-doctoral position we will explore in particular ensemble methods for Koopman representation and data assimilation. At first we will work on deterministic dynamics on the basis of a recent theoretical framework we proposed based on reproducing Hilbert spaces together with ergodic theory and group theoretic framework. We plan then to extend this framework to the location under uncertainty context. Compétences She/he must have a good knowledge of Fortran, C/C+/ Python, Pytorch. Avantages - Subsidized meals - Partial reimbursement of public transport costs - Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.) - Social, cultural and sports events and activities Rémunération Monthly gross
